Connecting Image Similarity Retrieval with Consistent Labeling Problem by Introducing a Match-all Label

Abstract

Our work in image similarity retrieval by relaxation labeling processes required adding local constraints to obtain an initial set of compatible objects and labels on pairs of images to ensure labeling consistency at convergence. This approach suffers from the problem of over-specified constraints that leads to potentially invaluable information being prematurely removed. To address this problem, we introduce the idea of a Match-all label for objects that failed these constraints. It serves to give them a defined labeling probability as well as allowing them participate in global correspondences through the compatibility model. We show that this enhanced formulation still meets the conditions for a theorem on labeling consistency in Hummel and Zucker to be satisfied. At convergence, the set of objects and their most consistent labels constitute a best partial labeling for the pair of images.